Delayed vehicle identification for privacy enforcement

ABSTRACT

A method for recognition of an identifier such as a license plate includes storing first visual signatures, each extracted from a first image of a respective object, such as a vehicle, captured at a first location, and first information associated with the first captured image, such as a time stamp. A second visual signature is extracted from a second image of a second object captured at a second location and second information associated with the second captured image is acquired. A measure of similarity is computed between the second visual signature and at least some of the first visual signatures to identify a matching one. A test is performed, which is a function of the first and the second information associated with the matching signatures. Only when it is confirmed that the test has been met, identifier recognition is performed to identify the identifier of the second object.

BACKGROUND

The exemplary embodiment relates to object recognition and findsparticular application in connection with a system and method fordelaying the identification of an identifier for a vehicle until acondition is satisfied.

Vehicle identification can be defined as the process of measuring a setof properties of a vehicle with one or multiple sensors and mapping itto a unique identifier. Visual sensors (e.g., cameras) are often used tocapture an image containing a license plate and then an AutomaticLicense Plate Recognition (ALPR) process is applied. Given an imagecontaining a vehicle license plate, ALPR systems typically output thelicense plate number of the vehicle, which constitutes a uniqueidentifier. The recognized identifier can be used for differentpurposes, such as automatic collection of tolls, enforcement, such asdetection of speed violations, management of car parks, data mining foranalysis of traffic patterns, and the like.

However, automatic vehicle identification poses some concerns forprivacy rights, since the acquirer of the vehicle identifier, such as acar park owner, possesses data that can often be traced to the vehicleowner. There is also a risk that unauthorized persons may be able toaccess the data and use it for purposes other than the legitimatepurposes for which it was collected.

In some countries, therefore, governments and citizens are becoming moreconcerned about the collection of such data and restrictions are beingplaced on its collection. The storage of a vehicle identifier inpersistent memory has been recognized as a violation of the right toprivacy in some European countries. To address this, number platerecognition system have been developed that place each passing vehicleidentifier in temporary storage and access it directly in real-time tocheck for exceptional situations (such as a vehicle exceeding a speedlimit). If there is no match, the data is not permanently stored.

Even where there is no government regulation, there is pressure tomodify the process of automatic vehicle identification in car parks andother locations, including processing and storage of the identities, inorder to preserve the privacy of the car park users.

There remains a need for a system and method which provides for therecognition of a unique identifier of an object, such as a vehicle,while preserving the privacy of persons associated with the identifierwhere there is no specific need to process the identifier.

INCORPORATION BY REFERENCE

The following references, the disclosures of which are incorporatedherein by reference in their entireties, are mentioned:

U.S. Pub. No. 20100226564, entitled FRAMEWORK FOR IMAGE THUMBNAILINGBASED ON VISUAL SIMILARITY, by Luca Marchesotti, et al.

U.S. Pub. No. 20120143853, published on Jun. 7, 2012, entitledLARGE-SCALE ASYMMETRIC COMPARISON COMPUTATION FOR BINARY EMBEDDINGS, byAlbert Gordo, et al.

U.S. Pub. No. 20130060786, published Mar. 7, 2013, entitled TEXT-BASEDSEARCHING OF IMAGE DATA, by Jose Antonio Rodriguez Serrano, et al.

U.S. Pub. No. 20130129151, published May 23, 2013, entitled METHODS ANDSYSTEMS FOR IMPROVED LICENSE PLATE SIGNATURE MATCHING BY SIMILARITYLEARNING ON SYNTHETIC IMAGES, by Jose Antonio Rodriguez Serrano, et al.

U.S. Pub. No. 20130129152, published May 23, 2013, entitled METHODS ANDSYSTEMS FOR IMPROVING YIELD IN WANTED VEHICLE SEARCHES, by Jose AntonioRodriguez Serrano, et al.

U.S. Pub. No. 20130182909, published Jul. 18, 2013, entitled IMAGESEGMENTATION BASED ON APPROXIMATION OF SEGMENTATION SIMILARITY, by JoseAntonio Rodriguez Serrano.

U.S. Pub. No. 20130259314, published Oct. 3, 2013, entitled METHODS ANDSYSTEMS FOR ENHANCING THE PERFORMANCE OF AUTOMATED LICENSE PLATERECOGNITION APPLICATIONS UTILIZING MULTIPLE RESULTS, by VladimirKozitsky, et al.

U.S. application Ser. No. 13/527,228, filed Jun. 19, 2012, entitledOCCUPANCY DETECTION FOR MANAGED LANE ENFORCEMENT BASED ON LOCALIZATIONAND CLASSIFICATION OF WINDSHIELD IMAGES, by Sandra Skaff, et al.

U.S. application Ser. No. 13/592,961, filed on Aug. 23, 2012, entitledREGION REFOCUSING FOR DATA-DRIVEN OBJECT LOCALIZATION, by Jose AntonioRodriguez Serrano.

U.S. application Ser. No. 13/757,014, filed on Feb. 1, 2013, entitledLABEL-EMBEDDING FOR TEXT RECOGNITION, by Jose Antonio Rodriguez Serrano,et al.

U.S. application Ser. No. 13/836,310, filed on Mar. 15, 2013, entitledMETHODS AND SYSTEM FOR AUTOMATED IN-FIELD HIERARCHICAL TRAINING OF AVEHICLE DETECTION SYSTEM, by Wencheng Wu, et al.

U.S. application Ser. No. 13/903,218, filed May 28, 2013, entitledSYSTEM AND METHOD FOR OCR OUTPUT VERIFICATION, by Jose Antonio RodriguezSerrano, et al.

U.S. application Ser. No. 13/973,330, filed Aug. 22, 2013, entitledSYSTEM AND METHOD FOR OBJECT TRACKING AND TIMING ACROSS MULTIPLE CAMERAVIEWS, by Edgar A. Bernal, et al.

BRIEF DESCRIPTION

In accordance with one aspect of the exemplary embodiment a method forrecognition of an identifier. The method includes, for each of aplurality of objects, storing a first visual signature, which has beenextracted from at least a part of a first image of the object capturedat a first location, and first information associated with the firstcaptured image. A measure of similarity is computed between a secondvisual signature and at least some of the first visual signatures toidentify a matching one of the first visual signatures, based on thecomputed measure of similarity. The second visual signature has beenextracted from at least a part of a second image of a second objectcaptured at a second location, based on at least a part of the secondimage. The stored first information associated with the matching one ofthe first visual signatures and stored second information associatedwith the second captured image are both retrieved. A test is performedwhich is a function of the retrieved stored first information and thestored second information. The method further includes providing forrecognizing an identifier of the second object based on a captured imageof the second object, when it is confirmed that the test is met. One ormore of the steps of the method may be performed with a computerprocessor.

In accordance with another aspect of the exemplary embodiment a systemfor recognition of an identifier includes memory which, for each of aplurality of objects, stores first and second visual signatures. Thefirst visual signature has been extracted from at least a part of afirst image of the object captured at a first location. The secondvisual signature has been extracted from at least a part of a secondimage of the object captured at a second location. Memory also storesfirst information associated with the first visual signature, and secondinformation associated with the second visual signature. A signaturematching component computes a measure of similarity between one of thesecond visual signatures and each of at least some of the first visualsignatures to identify a matching one of the first visual signatures,based on the computed measure of similarity. An information processingcomponent performs a test which is a function of the retrieved storedfirst information associated with the matching one of the first visualsignatures and the stored second information associated with the one ofthe second visual signatures. When it is confirmed that the test is met,an identifier recognition component recognizes an identifier of one ofthe objects based on a captured image of the second object. A processorimplements the signature matching component, the information processingcomponent, and the identifier recognition component.

In accordance with another aspect of the exemplary embodiment a methodfor recognition of a vehicle identifier includes, for each of aplurality of vehicles, capturing a first image of the vehicle at a firstlocation at a first time and capturing a second image of the vehicle ata second location at a second time, later than the first time, acquiringfirst information associated with the capture of the first image andsecond information associated with the capture of the second image, thefirst and second information each comprising a respective time stamp. Afirst visual signature is computed based on at least a part of the firstimage of the vehicle. A second visual signature is computed based on atleast a part of the first image of the vehicle. Each of the first visualsignatures is stored, linked to the respective first information. Eachof the second visual signatures is stored, linked to the respectivesecond information. A measure of similarity between one of the secondvisual signatures and at least some of the first visual signatures iscomputed to identify a matching one of the first visual signatures,based on the computed measure of similarity. The stored firstinformation associated with the matching one of the first visualsignatures and stored second information associated with the one of thesecond visual signatures are both retrieved. A test is performed whichis a function of the time stamps of the retrieved stored firstinformation and the retrieved stored second information. Provision ismade for recognizing an identifier of one of the vehicles based on thecaptured second image of the vehicle when it is confirmed that the testis met. One or more of the steps of the method may be performed with acomputer processor.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an overview of the exemplary system and method;

FIG. 2 is a functional block diagram of an environment in which a systemfor delayed recognition of an object identifier operates in accordancewith one exemplary embodiment;

FIG. 3 graphically illustrates computing of visual signatures in amethod for delayed recognition of an object identifier in accordancewith another exemplary embodiment;

FIG. 4 is a flow chart illustrating a method for delayed recognition ofan object identifier in accordance with another exemplary embodiment;

FIG. 5 is a plot showing accuracy of different vehicle identificationmethods in one example; and

FIG. 6 is a screenshot illustrating errors in two different vehicleidentification methods in the example.

DETAILED DESCRIPTION

With reference to FIG. 1, an overview of an exemplary system and methodfor privacy-preserving delayed recognition of an object identifier isshown. In the exemplary embodiment, the object is a vehicle, such as amotorized vehicle, and its identifier is or includes the license platenumber of the vehicle. However, it is to be appreciated that othermoving objects and their identifiers are also contemplated, such aspeople and their respective fingerprints and/or eye scans, shippingparcels and their tracking or address information, and the like. FIG. 1includes the numbers 1 to 8 which illustrate the order in which steps ofthe exemplary method may be performed, although these are not intendedto be limiting.

Vehicle identification may be desired at multiple locations and/or atdifferent times, for example at the entry and exit of a car park or atdifferent points on a road. For the sake of simplification, twolocations X and Y which are spaced from each other by a distance Z areconsidered, although it is to be appreciated that X and Y may be at orclose to the same location.

In one embodiment, the aim is to find correspondences between thevehicle identities at X and Y, and when a correspondence between twoidentities has been established, to determine whether a certaincondition is met that depends on information captured at X and Y. Onlyif the condition is met, a specified action is triggered, such aslicense plate recognition.

For example, in a speed enforcement application, if a correspondencebetween vehicle identities at X and Y is established from imagescaptured at X and Y, the information associated with the captured imagesmay include timestamps (T_(X), T_(Y)) at which the vehicle was presentat X and Y and respective geographical locations (L_(X), L_(Y)), e.g.,geographical coordinates of X and Y. The condition checked may bewhether the identified vehicle's speed exceeds a threshold. The speedmay be the average speed, computed as the distance Z between L_(Y) andL_(Y) divided by the time difference T_(Y)−T_(X). If the condition ismet, e.g., the speed exceeds a predefined threshold, the identifier isrecognized from the captured image of the vehicle, at point Y, forexample. A further action triggered may be to implement a process, suchas generating an alert that the identified vehicle incurred a speedviolation and launching an appropriate corresponding process, such asissuance of a speeding ticket.

In standard approaches based on Automatic License Plate Recognition(ALPR), the identities of all vehicles whose images are captured arerecognized. In contrast, the present system and method provides forrecognizing the identity only of violating vehicles, e.g., for billingpurposes. Since it is unnecessary to recognize the identity ofnon-violating vehicles, the present system and method respects theanonymity of the vast majority of the vehicles (the non-violating ones)and identifies vehicles only when necessary.

In the exemplary embodiment, the recognition processing of a movingobject, such as a vehicle, is split into two distinct operations:

1. Vehicle matching between points X and Y, which does not rely onrecognition of the identifier (e.g., through ALPR).

2. Recognition of the identity of a vehicle, whether at X, at Y or atboth locations, e.g., relying on ALPR.

It has been found that operation 1) performed without license platerecognition can be quite satisfactory for vehicle matching. For example,as demonstrated in the example below, it can be more accurate thanidentifying the vehicle at the two points with ALPR and then checkingfor exact matches of the license plate number.

In an exemplary embodiment, the vehicle matching is performed bysignature matching. A signature is a set of values that represents thevehicle, or a part thereof. The signatures of two occurrences of thesame vehicle should be more similar than the signatures of two differentvehicles. Two vehicles are considered to have the same identity if thesimilarity between signatures exceeds a predefined threshold.

The exemplary signature may be derived from a set of visual featurescomputed from the license plate region of the image, for example aFisher Vector. In this case, similarity between signatures can becomputed by a vector-to-vector similarity. Other visual cues may be usedto complement the visual features, such as the vehicle make and/or modelor distinctive features of the vehicle, such as the presence of ascratch (shape of the scratch, location, etc.). The availability ofthese cues may depend, in part, on how much of the car is visible in thecaptured images.

One advantage of using visual signatures is that a determination can bemade as to whether two signatures correspond to the same vehicle (ornot), without explicitly attempting to identify the vehicle. As anexample, for each vehicle driving through a point Y, an attempt can bemade to find a match between its signature computed at point Y and asignature at point X. When a match is found, the associated data (suchas timestamps) is extracted and used to check the appropriate condition.Throughout this processing, the vehicle remains anonymous to the system.Only if the condition is met, an ALPR identification of the vehicle isrequested and the corresponding action is triggered.

In addition to the privacy-preserving aspect, in terms of accuracy, ithas been found that direct signature matching tends to be more robustthan matching two ALPR outputs.

FIG. 2 illustrates an exemplary system 10 for delayed recognition of anobject identifier in an operating environment which may operatesubstantially as shown in FIG. 1. In the illustrations, the identifiersof vehicles have been anonymized.

The system 10 receives, as inputs, sets 12, 14 of images I_(X), I_(Y)captured at locations X and Y by image capture devices 16, 18, such ascameras, positioned at these locations, e.g., via a network interface19. Image capture devices 16, 18, may be configured to captureautomatically an image of each vehicle as it passes the respectivelocation, or only some of the vehicles, depending on the application.Each captured image I_(X), I_(Y) includes at least a portion of avehicle 20, 22 which is capable of moving between the two locations X,Y,e.g., along a predefined path 24, such as a road, bus route, car parklane, or the like between the two locations. Each image capture device16, 18, or a separate information capture component 26, 28, capturesinformation 30, 32 associated with the capture of each image, e.g., inthe form of image metadata M_(X), M_(Y), such as a respective timestampand/or other types of information available. In the exemplaryembodiment, location X is upstream on the path from location Y, suchthat for a given vehicle, image I_(X) and its associated metadata M_(X)is captured earlier in time than image I_(Y) and its associated metadataM_(Y).

The system includes memory 40 which stores instructions 42 forprocessing the images 12, 14 and associated metadata 30, 32. A processor44, in communication with the memory 40, executes the instructions 42.The illustrated instructions 40 include a signature computationcomponent 44, a signature matching component 46, an informationprocessing component 48, an identifier recognition component 50 and aprocess implementation component 52.

Briefly, the signature computation component 44 receives as input animage I_(X) of each vehicle 20, 22 captured at location X and extracts avisual signature V_(X) 62 from each image I_(X). If the image 12received is of a substantial portion of the vehicle, e.g., asillustrated in FIG. 3, the signature computation component 44 mayinclude a cropping component which automatically extracts a croppedimage 60 from the received image 12, which is predicted to include anobject of interest (e.g., the area including the license plate number).The signature computation component 44 then computes the visualsignature, e.g., based solely on the pixels of the cropped region, andstores the computed visual signature V_(X) in a database 64. Aftercomputing the signature V_(X), the original image 12 and cropped image60 are not retained and may be deleted from memory or may be stored inan encrypted form as an encrypted image 66.

As will be appreciated, some preprocessing of the captured image 12, 60may be performed at any time before and/or during the computing of thevisual signature, such as scaling, skew removal, reducing the pixelresolution, image enhancement techniques, conversion from color tomonochrome, and the like.

The signature computation component 44, or a separate signaturecomputation component, similarly extracts a visual signature V_(Y) fromimage I_(Y), for example, by generating a cropped image 70 andextracting a visual signature 72 therefrom, which may be stored in thedatabase 64, a separate database, or in temporary memory of the system.

The signature matching component 46 accesses the database 64 with eachnew visual signature V_(Y) to identify one or more matching signaturesfrom the visual signatures V_(X) computed for vehicles whose images werepreviously captured at location X. The signature matching componentretrieves the visual signature V_(X) that is the closest match (referredto as V*), together with its associated meta-data M_(X) (referred to asM*).

The information processing component 48 performs a test to determinewhether a condition is met, which is a function of M_(Y) and M*. Forexample, component 48 computes an average speed based on the timedifference between M* and M_(Y). Only if the test is satisfied, e.g., ifthe result of the test is positive, does the information processingcomponent 48 output information 74 which confirms that the test has beenmet, which may include a call to the identifier recognition component52. If the test is not met, the identifier recognition component doesnot receive a call to perform identifier recognition.

The identifier recognition component 52 (assuming the test is met)performs character recognition (ALPR) on the image I_(Y) 70 to generatea character sequence 76 corresponding to the recognized identifier ofthe vehicle, such as the license plate number. The processimplementation component 52 may thereafter take an appropriate action,e.g., output an evidence package 78 which may include one or more of:the output of the signature matching component (e.g., an identifierwhich allows the original image I_(X), to be retrieved, if required),the output 74 of the information processing component, and the output 76of the identifier recognition component. The package 78 may be outputfrom the system 10 via a network interface 79 and/or stored in memory 40for further processing. This may include, for example, the automatedgeneration of a notice that a violation has taken place, such as aspeeding ticket, unpaid vehicle toll, parking ticket, or the like,depending on the application.

The system 10 may communicate with external devices, such as imagecapture devices 16, 18, via wired or wireless connections 80 such as atelephone line, local area network, or a wide area network, such as theInternet. In some embodiments, data 12, 30, 14, 32 from the imagecapture devices is routed to the system 10 via an intermediate server.Hardware components 19, 40, 44, 79 of the system may communicate via adata/control bus 82.

The system 10 may be resident on one or more computing devices 84, suchas a PC, such as a desktop, a laptop, palmtop computer, portable digitalassistant (PDA), server computer, cellular telephone, tablet computer,pager, combination thereof, or other computing device capable ofexecuting instructions for performing the exemplary method. As will beappreciated parts of the system 10 may be distributed over two or morecomputing devices. For example, image signatures for images 12 may becomputed by a separate computing device from device 84, and stored indatabase 64 accessible to the computing device 84.

The system 10 may communicate, via input/output interface 79, with oneor more of a display device 86, such as an LCD screen or computermonitor, for displaying information to users, and a user input device88, such as a keyboard or touch or writable screen, and/or a cursorcontrol device, such as mouse, trackball, or the like, for inputtingtext and for communicating user input information and command selectionsto the processor 44. The display device and user input device areillustrated as being part of a client computing device 90, although inother embodiments, they may be directly linked to the computer 84.

The memory 40 may represent any type of non-transitory computer readablemedium such as random access memory (RAM), read only memory (ROM),magnetic disk or tape, optical disk, flash memory, or holographicmemory. In one embodiment, the memory 40 comprises a combination ofrandom access memory and read only memory. In some embodiments, theprocessor 44 and memory 40 may be combined in a single chip.

The network interface 19, 78 allows the computer to communicate withother devices via a computer network, such as a local area network (LAN)or wide area network (WAN), or the Internet, and may comprise amodulator/demodulator (MODEM) a router, a cable, and and/or Ethernetport.

The digital processor 44 can be variously embodied, such as by asingle-core processor, a dual-core processor (or more generally by amultiple-core processor), a digital processor and cooperating mathcoprocessor, a digital controller, or the like. The digital processor44, in addition to controlling the operation of the computer 84,executes instructions stored in memory 40 for performing the methodoutlined in FIG. 4.

The term “software,” as used herein, is intended to encompass anycollection or set of instructions executable by a computer or otherdigital system so as to configure the computer or other digital systemto perform the task that is the intent of the software. The term“software” as used herein is intended to encompass such instructionsstored in storage medium such as RAM, a hard disk, optical disk, or soforth, and is also intended to encompass so-called “firmware” that issoftware stored on a ROM or so forth. Such software may be organized invarious ways, and may include software components organized aslibraries, Internet-based programs stored on a remote server or soforth, source code, interpretive code, object code, directly executablecode, and so forth. It is contemplated that the software may invokesystem-level code or calls to other software residing on a server orother location to perform certain functions.

With reference to FIG. 4, a method for delayed recognition of anidentifier which may be performed with the system of FIG. 2 is shown.The method begins at S100.

At S102, a first image I_(X) of each vehicle captured at location X isreceived.

At S104, a visual signature V_(X) is extracted from first image I_(X),by the signature computation component 44.

At S106, metadata M_(X) associated with the captured first image isextracted, e.g., by the metadata extraction component 26 of the firstimage capture device 16 and/or by the information processing component48.

At S108, the visual signature V_(X) and the associated metadata M_(X)for each first image are stored in database 64, which is used to collectsuch data for many vehicles passing through location X. Each visualsignature V_(X) is linked to the respective information M_(X) so thatthe information M_(X) can later be retrieved based on the visualsignature V_(X), e.g., through an index which links the two.

Each of a set of visual signatures V_(X) may then be considered as acandidate match for a later-acquired image I_(Y). In some embodiments,the database 64 may be pruned periodically, for example, to removevisual signatures V_(X) and associated information M_(X) that have beenstored for longer than a predetermined period, or when it is unlikely tobe relevant. For example, the database may be pruned hourly or daily inthe case of an application which is designed to check speed, as even ifa vehicle image is subsequently matched, a threshold time delay would betoo large for the speed condition to be met.

At S110, access to the original (and cropped) image I_(X) may berestricted or denied, for example, by deleting or encrypting imageI_(X).

At S112, a second image I_(Y) of each vehicle captured at location Y isreceived.

At S114, a visual signature V_(Y) is extracted from second image I_(Y).

At S116, metadata My associated with the captured end image isextracted.

At S118, the visual signature V_(Y) and the associated metadata M_(Y)are stored, e.g., in database 64 and/or in temporary memory of thesystem 10. Each visual signature V_(Y) is linked to the respectiveinformation M_(Y).

At S120, for each visual signature V_(Y), a search is made in thedatabase 64 for similar visual signatures V_(X) and the closest match V*is identified. In some cases, a threshold similarity may be established,and image signatures which do not at least meet the threshold similarityare not considered as a match.

At S122, the metadata M* for the matching signature(s) V* is/areretrieved. At S124 a test is performed which includes a condition basedon M_(Y) and M*, to determine whether the test is met.

At S126, if is confirmed that the test is met (if and only if the testis satisfied), then the method continues to S128, where the identifierfor the vehicle is recognized, for example, by performing ALPR on I_(Y)(and/or I_(X)). Otherwise, the method proceeds to S130, where if thereare more image signatures V_(Y) to be tested, the method returns toS120, otherwise the method ends at S132.

The method may proceed from S128 to S134, where information 78 based onthe recognized identifier is output and/or used in a furthercomputer-implemented process. The method may then proceed to S130 orS132.

The method illustrated in FIG. 4 may be implemented in a computerprogram product that may be executed on a computer. The computer programproduct may comprise a non-transitory computer-readable recording mediumon which a control program is recorded (stored), such as a disk, harddrive, or the like. Common forms of non-transitory computer-readablemedia include, for example, floppy disks, flexible disks, hard disks,magnetic tape, or any other magnetic storage medium, CD-ROM, DVD, or anyother optical medium, a RAM, a PROM, an EPROM, a FLASH-EPROM, or othermemory chip or cartridge, or any other non-transitory medium from whicha computer can read and use. The computer program product may beintegral with the computer 84, (for example, an internal hard drive ofRAM), or may be separate (for example, an external hard driveoperatively connected with the computer 84), or may be separate andaccessed via a digital data network such as a local area network (LAN)or the Internet (for example, as a redundant array of inexpensive ofindependent disks (RAID) or other network server storage that isindirectly accessed by the computer 84, via a digital network).

Alternatively, the method may be implemented in transitory media, suchas a transmittable carrier wave in which the control program is embodiedas a data signal using transmission media, such as acoustic or lightwaves, such as those generated during radio wave and infrared datacommunications, and the like.

The exemplary method may be implemented on one or more general purposecomputers, special purpose computer(s), a programmed microprocessor ormicrocontroller and peripheral integrated circuit elements, an ASIC orother integrated circuit, a digital signal processor, a hardwiredelectronic or logic circuit such as a discrete element circuit, aprogrammable logic device such as a PLD, PLA, FPGA, Graphical card CPU(GPU), or PAL, or the like. In general, any device, capable ofimplementing a finite state machine that is in turn capable ofimplementing the flowchart shown in FIG. 4, can be used to implement themethod for delayed identifier recognition. As will be appreciated, whilethe steps of the method may all be computer implemented, in someembodiments one or more of the steps may be at least partially performedmanually.

Further details of the system and method will now be described.

Image and Metadata Capture (S102, S112, S106, S116)

Captured images 12, 14 (I_(X) and I_(Y)) may be received by the system10 in any convenient file format, such as JPEG, GIF, JBIG, BMP, TIFF, orthe like or other common file format used for images and which mayoptionally be converted to another suitable format prior to processing.The input images may be stored in data memory during processing. Theimages may be individual images, such as photographs, or imagesextracted from sequences of images, such as video images. In general,each input digital image includes image data for an array of pixelsforming the image. The image data may include colorant values, such asgrayscale values, for each of a set of color separations, such as L*a*b*or RGB, or be expressed in another other color space in which differentcolors can be represented. In general, “grayscale” refers to the opticaldensity value of any single color channel, however expressed (L*a*b*,RGB, YCbCr, etc.). The method is also suitable for black and white(monochrome) images. The word “color” is used to refer to any aspect ofcolor which may be specified, including, but not limited to, absolutecolor values, such as hue, chroma, and lightness, and relative colorvalues, such as differences in hue, chroma, and lightness. In someembodiments, color can refer to a non-visible region of theelectromagnetic spectrum, such as the Near Infrared (NIR) region, whichis from about 800 nm to 2500 nm.

The image capture at a given location X, Y may be triggered in anysuitable manner. In one embodiment, a loop sensor may be locallypositioned, e.g., in the ground, which detects the presence of a vehicleand triggers a shot (and possibly a flash). In one embodiment, thecamera 16, 18 shoots a video comprising a sequence of images and amotion detection algorithm or an object detection algorithm (or thecombination of both) is employed which detects the presence of a vehiclein the image sequence and selects one image from the sequenceaccordingly. Vehicle detection techniques are well known and disclosedfor example, in U.S. Pat. Nos. 4,433,325, 5,083,200, 5,592,567,5,809,161, 5,995,900, 6,996,255, and U.S. application Ser. Nos.13/836,310, and 13/973,330, the disclosures of all of which areincorporated herein in their entireties by reference. The image can becaptured in full color, monochrome, NIR (near infrared), or acombination thereof.

In some embodiments, the same image capture device could be used forcapturing both images I_(X) and I_(Y), such as in a car park.

The associated metadata 30, 32 may each include sufficient informationto enable a determination to be made as to whether a predeterminedcondition has been met. One or more different types of information maybe acquired, such as one or more of time stamp, current speed of thevehicle, GPS location, payment information, weather information, and thelike.

Visual Signature Computation (S104, S114)

In the exemplary embodiment, the visual signature 62, 72 contains enoughinformation to identify a vehicle (or part of a vehicle) uniquely butshould also robust enough to accommodate variations in viewpoint orlighting conditions. It is also constructed in such a way that, given avisual signature, neither the original image nor the identifier can bereconstructed from it.

In the exemplary embodiment, the visual signature 62, 72 is extractedfrom a region of the image which is predicted to contain the identifiersuch as a region which encompasses the license plate or at least thatpart of it which includes the license plate number. To identify thisregion, a detection algorithm that extracts the corresponding part ofthe image can be run. Methods for identifying the region of interest inthe larger image are described, for example, in U.S. Pub. No.20130182909 and application Ser. No. 13/592,961, incorporated herein byreference.

Once the region of interest has been identified and a cropped image 60,70 generated which includes this region, the visual signature 62, 72 canbe computed on the cropped region. In general, the visual signature is astatistical representation of the pixels of the cropped image. Forexample, a set of patches of the cropped image are extracted, e.g.,densely, on a grid at one or at multiple scales. The patches can beobtained by image segmentation, by applying specific interest pointdetectors, by considering a regular grid, or simply by the randomsampling of image patches. In the exemplary embodiment, the patches areextracted on a regular grid, optionally at multiple scales, over theentire cropped image, or at least a part or a majority of the image. Forexample, at least 10 or at least 20 or at least 50 patches are extractedfrom each cropped image. Each patch may comprise at least 40 or at least100 pixels, and up to 1,000,000 pixels or more.

For each patch, low-level features are extracted, such as shape, colorand/or gradient (SIFT) features (see, D. Lowe, “Distinctive imagefeatures from scale-invariant keypoints”, IJCV, 2004). A patchdescriptor, such as a vector or histogram, which is a representation ofthe extracted low level features for a patch, is generated. Based on thedescriptors of all the patches, on overall visual signature of the imageis generated. In particular, statistics are computed on these patchdescriptors and then the statistics are aggregated.

Each visual signature is a fixed-length vectorial representation of the(cropped) image in a D-dimensional space. In one embodiment, the visualsignature is based on the Fisher Vector (FV). See, for example,Perronnin and Dance, “Fisher kernels on visual vocabularies for imagecategorization,” CVPR, 2007; Perronnin, Sanchez and Mensink, “Improvingthe Fisher kernel for large-scale image classification”, ECCV, 143-156(2010); Sanchez and Perronnin, “High-dimensional signature compressionfor large-scale image classification,” in CVPR 2011; U.S. Pub. No.20120076401, published Mar. 29 2012, entitled IMAGE CLASSIFICATIONEMPLOYING IMAGE VECTORS COMPRESSED USING VECTOR QUANTIZATION, by JorgeSanchez, et al.; and U.S. Pub. No. 20120045134, published Feb. 23, 2012,entitled LARGE SCALE IMAGE CLASSIFICATION, by Florent Perronnin, et al.the disclosures of which are incorporated herein by reference in theirentireties. The trained GMM is intended to describe the content of anyimage within a range of interest (for example, any license plate imageif the range of interest is license plates). A Fisher Kernelrepresentation can be generated by aggregation of the Fisher vectors.Other image representations may be used such as the bag-of-visual-wordsof Csurka et al., Visual Categorization with Bags of Keypoints, ECCVWorkshop, 2004.

Fisher Vectors show robustness in the range of photometric and geometricvariability found in license plate processing. Briefly, Fisher vectorswork by aggregating local patch descriptors into a fixed-lengthrepresentation. First, SIFT descriptors are extracted from patchesextracted at multiple scales on a regular grid, and their dimensionalityis optionally reduced using principal component analysis (PCA). A visualvocabulary is built by estimating a Gaussian mixture model (GMM) withpatch descriptors extracted from a held-out set of images. In otherapproaches, the local descriptors of the patches of an image areassigned to clusters. For example, a visual vocabulary is previouslyobtained by clustering local descriptors extracted from training images,using for instance K-means clustering analysis. Each patch vector isthen assigned to a nearest cluster and a histogram of the assignmentscan be generated. In other approaches, a probabilistic framework isemployed, as in the case of the Fisher vector described above. Forexample, it is assumed that there exists an underlying generative model,such as a Gaussian Mixture Model (GMM), from which all the localdescriptors are emitted. Each patch can thus be characterized by avector of weights, one weight for each of a set of (e.g., at least 5 or10) Gaussian functions forming the mixture model. In this case, thevisual vocabulary can be estimated using the Expectation-Maximization(EM) algorithm. In either case, each visual word in the vocabularycorresponds to a grouping of typical low-level features. The visualwords may each correspond (approximately) to a mid-level image featuresuch as a type of visual (rather than digital) object (e.g., features ofcharacters, such as straight lines, curved lines, etc.), characteristicbackground (e.g., light or dark surface, etc.), or the like. Given animage to be assigned a visual signature, each extracted local descriptoris assigned to its closest visual word in the previously trainedvocabulary or to all visual words in a probabilistic manner in the caseof a stochastic model. A histogram is computed by accumulating theoccurrences of each visual word. The histogram can serve as the visualsignature or input to a generative model which outputs a visualsignature based thereon.

The Fisher vector is computed as the derivative of the log-likelihoodwith respect to the GMM parameters. For example, if only the means areconsidered, it can be shown that the expression is given by:

$f_{id} = {{\gamma \left( x_{t} \right)}\left\lbrack \frac{x_{t,d} - m_{i,d}}{\left( S_{i,d} \right)^{2}} \right\rbrack}$

where γ(x_(t)) is the soft-assignment probability of the tth patch tothe ith Gaussian, x_(t,d) is the dth component of the ith patch, andm_(i,d) and S_(i,d) are the dth components of the mean and standarddeviations of the ith Gaussian, assuming diagonal covariances. Here, i=1K and d=1 . . . D. If only the derivatives with respect to the mean areused, then the resulting Fisher vector is a concatenation of the K×Delements f_(id). Square-rooting and l₂-normalization of the vector maybe used.

To include spatial information about the cropped image, the image can bepartitioned into regions, such as at least three regions, the per-patchstatistics aggregated at a region level, and then the region-levelrepresentations concatenated to form the image representation. See, forexample, S. Lazebnik, et al., “Beyond bags of features: Spatial pyramidmatching for recognizing natural scene categories,” CVPR '06 Proc. 2006IEEE Computer Society Conf. on Computer Vision and PatternRecognition—Volume 2, Pages 2169-2178.

The exemplary visual signatures are of a fixed dimensionality D, i.e.,each image representation has the same number of elements. In general,each image representation has at least 30, or at least 60, or at least100, or at least 500 dimensions, and up to 1000 or more dimensions, eachdimension having a respective feature value, which may be reduced tofewer dimensions.

As an example, the low-level features include gradient features, such asSIFT descriptors, one per patch. See, e.g., Lowe, “Distinctive imagefeatures from scale-invariant keypoints,” IJCV vol. 60 (2004). In oneillustrative example employing SIFT features, the features are extractedfrom 32×32 pixel patches on regular grids (every 16 pixels) at fivescales. The dimensionality of these descriptors can be reduced from 128to 32 dimensions. Other suitable local descriptors which can beextracted include simple 96-dimensional color features in which a patchis subdivided into 4×4 sub-regions and in each sub-region the mean andstandard deviation are computed for the three channels (R, G and B).These are merely illustrative examples, and additional and/or otherfeatures can be used. In the examples below, a visual vocabulary of 64Gaussians is used in the GMM and only the gradient with respect to themean parameters is considered. The cropped image is split into 4 regions(4 vertical stripes). This results in a 32×64×4=8,192-dimensional FVrepresentation. The representations may be indexed or compressed usingconventional techniques (locality sensitive hashing (LSH), productquantization, principal component analysis (PCA), etc.) to speed up theprocess.

The visual signature is one which does not allow recognition from theidentifier from it. A visual signature such as the FV fulfills thedesired property that the original image cannot be reconstructed for it,and therefore can be seen as a hash key of the original image as thereis no feasible way to recover the original image (or the license platenumber) from the visual signature. This is because the identity of theindividual patches is lost in the aggregation process. Consequently,this reduces the privacy concern as the service operator does not haveaccess to the identities of all vehicles, only a very small portion ofthe vehicles that cause the actions to be triggered (e.g., vehiclesdriving above speed limit). Like hashing functions used for storingpasswords, the only method to recover the original image is to try toperform a brute-force match with all possible images.

Other exemplary methods for computing image representations aredisclosed for example, in the following references, the disclosures ofall of which are incorporated herein in their entireties, by reference:US Pub. Nos. 20030021481; 2007005356; 20070258648; 20080069456;20080240572; 20080317358; 20090144033; 20090208118; 20100040285;20100082615; 20100092084; 20100098343; 20100189354; 20100191743;20100226564; 20100318477; 20110026831; 20110040711; 20110052063;20110072012; 20110091105; 20110137898; 20110184950; 20120045134;20120076401; 20120143853, and 20120158739.

Storage (S108)

The signature/metadata pair (V_(X), M_(X)) is stored in the database 64linked together so that the signature matching component 46 can returnthe associated metadata whenever it finds a match between a signatureV_(X) and the visual signature V_(Y) of a new image I_(Y).

Restriction of Access (S110)

The controls on access to original image I_(X) and/or I_(Y) may dependon the application and/or on local privacy laws which restrict the useof personal information. In some applications, the original image may beretained in case it is needed for visual proof that the signaturematching was correct. However, it may be encrypted with an encryptionmethod, e.g., a public/private key method, which allows only authorizedusers to decrypt the encrypted images when permitted to do so, e.g.,when authorized by the vehicle owner or a law enforcement body ortribunal. In other embodiments, the original images I_(X) and allcopies/cropped versions thereof may be deleted from computer memory assoon as the image signature has been computed. Precautions may be toprevent human access to the captured images in the period between imagecapture and signature computation. In some embodiments, the imagesignature computation may be performed by software components within theimage capture devices 16, 18 to avoid the images themselves beingintercepted.

Where the image I_(X) is to be deleted or encrypted, this may take placeprior to at least one of steps S106 to S128 (if the test is met) orS130, if not met. In one embodiment, S110 may take place prior to S112.

Visual Search (S120)

Given a query visual signature V_(Y), the database 64 is searched forits closest match(es). The measure of similarity which is used maydepend on the type of visual signature employed. The cosine distance hasbeen shown to be an appropriate measure of similarity for the FV.However, other similarity measures are also contemplated, such as theEuclidian distance and the Manhattan distance. The Euclidian distance,dot product, the chi² distance, Kullback-Leibler (KL) divergence,Jensen-Shannon divergence, and the like may be used in computingsimilarity for other types of visual signature, for example in thebag-of visual words method.

When the number of visual signatures V_(X) stored in the database 64 issmall enough (e.g., on the order of several thousand visual signatures),then the search for a closest match V* can be done in an exhaustivemanner, i.e., by comparing the visual query with all database entries.When the database 64 contains a larger number of images, an exhaustivesearch may be time consuming. In this case, an approximate searchtechnique may be employed. Techniques for the approximate search ofhigh-dimensional vectors are disclosed, for example, in Jegou, et al.,“Aggregating local image descriptors into compact codes,” IEEE TPAMI,34(9)1704-1716 (2012).

Since the Fisher vector is an explicit embedding of the Fisher kernel,the corresponding cosine similarity measure between two such imagedescriptors V_(X) and V_(Y) is the dot product V_(X) ^(T)V_(Y). Acandidate plate is compared against all images in a database and theidentity of the closest match is assigned, provided the similarity issufficiently high.

In one embodiment, the visual signature (e.g., the FV) can be made morediscriminative by applying a projection which is obtained, for instance,by learning a metric for embedding the visual signature into a vectorialspace where similarity is a good indicator for actual similarity (interms of the character sequences forming the identifiers). As anexample, a low-rank Mahalanobis metric can be employed, see for example,Rodriguez-Serrano, et al., “Data-Driven Vehicle Identification by ImageMatching”. 12^(th) European Conf. on Computer Vision (ECCV) Workshops,Oct. 7-13, 2012, Lecture Notes in Computer Science, vol. 7584, pp.536-545. See also U.S. application Ser. Nos. 13/592,961, 13/757,014, and13/903,218, the disclosures of which are incorporated by reference, fordescriptions of exemplary embedding techniques. The FV has been shownexperimentally to be particularly appropriate for matching of licenseplate images, especially in combination with learned projections. TheFisher Vector is also shown below to be superior to the standard licenseplate matching approach which involves in performing ALPR and thencomparing the sequences.

Metadata Processing (S124)

The information processing component 48 may compute various metricsbased on the metadata collected at two (or more) locations. For example,the timestamps and GPS coordinates may be used to infer an averagespeed. This may be compared with a threshold speed, e.g., a maximumspeed limit, to determine whether the speed limit is exceeded. In thecase of information relating to weather conditions (e.g., a detectedrainfall or visibility), this may be used to determine whether theaverage speed of the vehicle exceeded a weather-related speed limit,which may be lower than the normal posted speed limit.

In some applications images captured at “entries” are associated to“tickets”. Thus the metadata can include a ticket ID, ticket status(paid, non-paid), paid amount, authorized time (e.g., based on the paidamount), and so forth. For example, if a ticket was paid for authorizingthe user to park for a time of 2 hours, but the user parked for 4 hours,an unpaid amount may be computed optionally with a penalty. In thiscase, the test condition applied at S124 may define a relationshipbetween the computed time and the authorized time, for example, to allowfor some leeway. For example, the condition may specify that the test ismet if the computed time exceeds the authorized time by at least apredetermined amount, such as 5, 10 or 15 minutes, or a percentage ofthe authorized time.

As will be appreciated, the test may include more than one condition anda given test may require that one or more (or all) of the conditions besatisfied for the test to be met. In some cases, conditions may beevaluated in the alternative. For example, one condition may specify: ifcondition 1 is satisfied, test condition 2, otherwise test condition 3.Different conditions may rely on the same or different parts of themetadata. For example, one condition could relate to the weather or timeof day/day of the week, and another condition to the computed speed ofthe vehicle, length of stay in the car park, and so forth.

ALPR (S128)

Only if the test performed on the metadata at S124 is positive (e.g.,speed greater than a posted speed limit, a parking duration which doesnot correspond to a paid amount, exiting a car park without payingticket), then identifier recognition, e.g., ALPR is performed. The ALPRmay be performed on image I_(Y) before or after cropping or otherprocessing. For example, any suitable ALPR system may be used forrecognizing the license plate number based in the cropped image 70.Where multiple images I_(Y) are obtained at location Y, as in a videosequence, these are considered to be the same as image I_(Y).

In the case of license plate number recognition, the identifier(sometimes referred to as a registration identifier) includes a sequenceof characters drawn from a predefined alphabet (a finite set ofcharacters), such as letters and numbers. The license plate number maybe a numeric or alphanumeric code that uniquely identifies the vehicleon which it is mounted within the issuing region's database.

The license plate recognition component 50 may use optical characterrecognition (OCR) alone or in combination with other techniques, toidentify a sequence of characters drawn from the finite alphabet that ispredicted to correspond to the characters in the cropped image 70.Spaces and characters other than those in the finite alphabet may beignored. In some embodiments, the recognition component 50 may extractadditional textual information, such as the state of issuance. Logos mayalso be recognized from a stored logo database.

As will be appreciated, the license plate number and image of a licenseplate are exemplary only and are used to illustrate the exemplaryembodiment. In other embodiments, a larger set of ASCII, UNICODE, and/orUTF-8 characters may be used as the alphabet.

License plate recognition methods which may be used are disclosed, forexample, in above-mentioned U.S. Pub. Nos. 20130129151, 20130129152,20130182909, and 20130259314, and U.S. application Ser. Nos. 13/592,961,13/757,014, 13/836,310, and 13/903,218, and in J-A. Rodriguez-Serrano, HSandhawalia, R. Bala, F. Perronnin and C. Saunders, “Data-Driven VehicleIdentification by Image Matching”. 12^(th) European Conf. on ComputerVision (ECCV) Workshops, Oct. 7-13, 2012, Lecture Notes in ComputerScience, vol. 7584, pp. 536-545. In one embodiment, Xerox License PlateRecognition (XLPR) software is employed.

Action (S134)

The action initiated by the process implementation component 52 maydepend on the type of application. In the case of license plates, theaction triggered is associated to the respective license plate numberidentified at S128 (e.g., prosecute the violator, alert parkingoperator, send parking fine, etc.).

Accounting for Imperfect Matching

In some embodiments, it is reasonable to assume that the visualsignature matching component 46 delivers 100% accuracy. In such a case,the following operations may be performed:

1. At location X, all the images captured at location X can be discardedafter signature computation.

2. At location Y, all the images and signatures of non-violatingvehicles can be discarded after S126.

However, a 100% accuracy is not always possible in practice. Forexample, the following sources of failure may be found. First, thevisual signature matching component 46 sometimes does not return anyresult at S122, for example, because the confidence in the closest matchis too low. In such a case, steps S124 and S126, etc., are simply notperformed. This also means that the corresponding signature which shouldhave been retrieved as V* remains in the database, possiblyindefinitely. To avoid the database becoming populated with signatureswhich may never be needed again, the database may be regularly purged toremove its oldest entries, for example, when they have been in thesystem for more than a predetermined amount of time. In the case wherethe condition tested at S124 cannot be met after a predetermined amountof time (for example, the computed speed can no longer exceed the speedlimit) the signatures V_(X) which cannot meet the specified conditioncan be purged.

A second source of failure is a false positive, where the signaturematching component 46 may return an incorrect result with a confidencewhich is higher than a predetermined confidence threshold. This may leadto a vehicle owner erroneously receiving a parking/speeding ticket orother action being taken. In such a case, it may be desirable to ensurethat a human operator can correct such an error, when the error becomesknown. For example, a procedure may be established for the driver of thevehicle, service provider, or law enforcement to challenge the system.To assist in verifying that a failure has occurred, the image I_(X) maybe stored in the database (e.g., in an encrypted format) and both I_(X)and I_(Y) may be included in the evidence package output at S134. Whilethe images may be stored in an unencrypted format, there remains a riskthat an unauthorized person may gain access to the identity of the userswho have been through X but not yet through Y by visually inspecting theimages in the database. To address this issue, the images may be storedin an encrypted format, e.g., by encrypting each image (and/or croppedimage) with a public key. Only the persons who are authorized to accessthe evidence package 78 (e.g., officers of the law) may have access tothe private key needed to decrypt the images.

Uses of the Exemplary System

Examples of where the exemplary system and method may find applicationinclude:

1. Point-to-point enforcement, for example speed enforcement on amedium-/long-range.

2. Monitoring entries and exits of car parks. When a vehicle exits thecar park, the signature matching component 46 associates its visualsignature with the entry transaction. Assuming a ticket is delivered tothe customer at the entry, the metadata can include the timestamp, theticket number, and payment status for that ticket. At the exit, themetadata processing at S124 checks that the ticket has been paid andthat the paid amount corresponds to the correct duration. It is alsopossible to ascertain if the ticket is associated with the same car atentry and exit to avoid ticket swapping. In car parks with barriers,that could trigger an alert to an operator to go and resolve the issue.In car parks without barriers, a fine could be sent to a customer.

3. Automatic tolling: this may include associating the vehicle ID withthe bank account/credit card of subscribed drivers for automaticpayments.

4. Violation enforcement: this may include associate the violatingvehicle ID with the identity of the vehicle owner for prosecution.

5. Car park management: this may include associating the vehicle ID withan issued parking ticket and open a barrier automatically at the carpark exit; or associate the vehicle ID to a subscribed customer accountand open all barriers automatically.

6. Vehicle flow analysis/data mining: This may include trackingre-occurring vehicle IDs to measure travel times, analyze patterns ofbehavior, and the like.

Without intending to limit the scope of the exemplary embodiment, thefollowing example illustrates the application of the method to licenseplate recognition

Example

The accuracy of image matching (using the exemplary visual signaturesusing Fisher Vectors) was evaluated as a method for identifying the samevehicle at entry and exit locations of a car park. The number of correctentry-exit associations was counted. The plot shown in FIG. 5 comparesthe accuracy-reject characteristic of signature matching with twoconventional ALPR systems (denoted ALPR 1 and ALPR 2). The visualsignature matching method correctly associates about 96% of the entriesand exits. In contrast, an OCR system which relies on exact matching ofthe sequence of characters identified in each image yields results withan accuracy below 90%. Although applying inexact OCR matching yieldsaccuracies similar to the signature matching method, it is not possibleto perform inexact matching if the license plate numbers are hashed.

FIG. 6 is a screenshot of a demonstration of data captured at a car parkused in the evaluation. The screenshot displays an image captured at theexit of the car park and the associated entry image retrieved bysignature matching and ALPR-based (OCR) matching. While both methodsretrieved the same image in this case, on average, the ALPR systemproduced more errors (11.2%) than the present visual signature matchingsystem (3.41%). Additionally, the present visual signature matchingmethod could be optimized further to account for such factors aslighting.

It will be appreciated that variants of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be combined intomany other different systems or applications. Various presentlyunforeseen or unanticipated alternatives, modifications, variations orimprovements therein may be subsequently made by those skilled in theart which are also intended to be encompassed by the following claims.

What is claimed is:
 1. A method for recognition of an identifier, comprising: for each of a plurality of objects, storing a first visual signature, which has been extracted from at least a part of a first image of the object captured at a first location, and first information associated with the first captured image; computing a measure of similarity between a second visual signature and at least some of the first visual signatures to identify a matching one of the first visual signatures, based on the computed measure of similarity, the second visual signature having been extracted from at least a part of a second image of a second object captured at a second location, based on at least a part of the second image; retrieving the stored first information associated with the matching one of the first visual signatures and stored second information associated with the second captured image; performing a test which is a function of the retrieved stored first information and the stored second information; and providing for recognizing an identifier of the second object based on a captured image of the second object, when it is confirmed that the test has been met.
 2. The method of claim 1, wherein at least one of the computing the measure of similarity, performing the test, and the recognizing of the identifier is performed with a computer processor.
 3. The method of claim 1, further comprising: for each of the plurality of objects: receiving the first image of the object captured at the first location and the information associated with the first captured image, extracting the first visual signature based on at least a part of the first image, and storing the first visual signature and the information associated with the first image.
 4. The method of claim 3, further comprising deleting or encrypting each first image after extracting the respective visual signature and at least prior to the recognizing of the identifier.
 5. The method of claim 1, further comprising: receiving the second image of the second object captured at the second location and the information associated with the second image; extracting the second visual signature based on at least a part of the second image.
 6. The method of claim 1, wherein each of the first and second visual signatures comprises a multidimensional statistical representation of pixels of at least the part of the respective image.
 7. The method of claim 1, wherein each object comprises a vehicle and each identifier comprises a sequence of characters.
 8. The method of claim 7, wherein the recognizing of the identifier comprises performing automated license plate recognition on the first or second image.
 9. The method of claim 1, wherein the first information comprises a first time stamp and the second information comprises a second time stamp which is later in time than the first time stamp.
 10. The method of claim 9, wherein the performing of the test comprises computing a speed based on the first and second time stamps and a distance between the first and second locations and comparing the computed speed to a threshold speed.
 11. The method of claim 10, wherein the test is met when the computed speed exceeds the threshold speed.
 12. The method of claim 9, wherein the performing of the test comprises comparing a difference between the first and second time stamps with an authorized time.
 13. The method of claim 12, wherein the test is met when the difference between the first and second time stamps exceeds the authorized time by a predetermined amount.
 14. The method of claim 1, further comprising implementing a process based on the recognized identifier.
 15. The method of claim 1, wherein first visual signatures are stored in a database and the method further comprises purging first visual signatures from the database when the test can no longer be met.
 16. The method of claim 1, wherein the second object is one of the plurality of objects.
 17. The method of claim 1, wherein when the test has not been met no recognition of the identifier is performed.
 18. A computer program product comprising a non-transitory recording medium storing instructions, which when executed on a computer causes the computer to perform the method of claim
 1. 19. A system comprising memory which stores instructions for performing the method of claim 1, and a processor in communication with the memory which executes the instructions.
 20. A system for recognition of an identifier, comprising: memory which, for each of a plurality of objects, stores: a first visual signature, which has been extracted from at least a part of a first image of the object captured at a first location, and first information associated with the first visual signature, and a second visual signature, which has been extracted from at least a part of a second image of the object captured at a second location, and second information associated with the second visual signature; a signature matching component for computing a measure of similarity between one of the second visual signatures and at least some of the first visual signatures to identify a matching one of the first visual signatures, based on the computed measure of similarity; an information processing component which performs a test which is a function of the retrieved stored first information associated with the matching one of the first visual signatures and the stored second information associated with the one of the second visual signatures; an identifier recognition component which recognizes an identifier of one of the objects based on a captured image of the second object when it is confirmed that the test is met; and a processor which implements the signature matching component, the information processing component, and the identifier recognition component.
 21. A method for recognition of a vehicle identifier, comprising: for each of a plurality of vehicles: capturing a first image of the vehicle at a first location at a first time; capturing a second image of the vehicle at a second location at a second time, later than the first time, acquiring first information associated with the capture of the first image, acquiring second information associated with the capture of the second image, the first and second information each comprising a respective time stamp, computing a first visual signature based on at least a part of the first image of the vehicle, computing a second visual signature based on at least a part of the second image of the vehicle, storing each of the first visual signatures linked to the respective first information, and storing each of the second visual signatures linked to the respective second information; computing a measure of similarity between one of the second visual signatures and at least some of the first visual signatures to identify a matching one of the first visual signatures, based on the computed measure of similarity; retrieving the stored first information associated with the matching one of the first visual signatures and stored second information associated with the one of the second visual signatures; performing a test which is a function of the time stamps of the retrieved stored first information and the stored second information; and when it is confirmed that the test is met, providing for recognizing an identifier of one of the vehicles based on the captured first or second image of the vehicle. 